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A clustered virtual machine allocation strategy based on a sleep-mode with wake-up threshold in a cloud environment

Author

Listed:
  • Shunfu Jin

    (Yanshan University)

  • Xiuchen Qie

    (Yanshan University)

  • Wenjuan Zhao

    (Yanshan University)

  • Wuyi Yue

    (Konan University)

  • Yutaka Takahashi

    (Kyoto University)

Abstract

The massive amount of energy consumed by cloud data centers is detrimentally impacting on the environments. As such, to work towards “greener” computing, in this paper, we propose a clustered virtual machine (VM) allocation strategy based on a sleep-mode with a wake-up threshold. The VMs in a cloud data center are clustered into two pools, namely, Pool I and Pool II. The VMs in Pool I remain awake at all times, while the VMs in Pool II go to sleep under a light workload. After a sleep timer expires, the corresponding VM will resume processing tasks only if the number of waiting tasks reaches the wake-up threshold. Otherwise, the sleeping VM will remain asleep as a new sleep timer starts. By establishing a queue with an N-policy and asynchronous vacations of partial servers, we capture the stochastic behavior of tasks with the proposed strategy, and derive the performance measures in terms of the average latency of tasks and the energy saving rate of the system. Furthermore, we provide numerical results to demonstrate the impact of the system parameters on the system performance. Finally, we construct a system cost function to trade off different performance measures, and develop an intelligent searching algorithm to jointly optimize the number of the VMs in Pool II, the wake-up threshold and the sleeping parameter.

Suggested Citation

  • Shunfu Jin & Xiuchen Qie & Wenjuan Zhao & Wuyi Yue & Yutaka Takahashi, 2020. "A clustered virtual machine allocation strategy based on a sleep-mode with wake-up threshold in a cloud environment," Annals of Operations Research, Springer, vol. 293(1), pages 193-212, October.
    • Handle: RePEc:spr:annopr:v:293:y:2020:i:1:d:10.1007_s10479-019-03339-3
    DOI: 10.1007/s10479-019-03339-3
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    References listed on IDEAS

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    1. Naishuo Tian & Zhe George Zhang, 2006. "Vacation Queueing Models Theory and Applications," International Series in Operations Research and Management Science, Springer, number 978-0-387-33723-4, September.
    2. Kunjie Yu & Xin Wang & Zhenlei Wang, 2016. "An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 831-843, August.
    3. Naishuo Tian & Zhe George Zhang, 2006. "Applications of Vacation Models," International Series in Operations Research & Management Science, in: Vacation Queueing Models Theory and Applications, chapter 0, pages 343-358, Springer.
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